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Recent approaches to collaborative filtering have concentrated on estimating an algebraic or statistical model, and using the model for predicting missing ratings. In this paper we observe that different models have relative advantages in different regions of the input space. This motivates our approach of using stagewise linear combinations of collaborative filtering algorithms, with non-constant combination coefficients based on kernel smoothing. The resulting stagewise model is computationally scalable and outperforms a wide selection of state-of-the-art collaborative filtering algorithms.
Author Information
Joonseok Lee (Google Research)
Joonseok Lee is a research engineer at Google Research. He is mainly working on content-based video recommendation and multi-modal video representation learning. He earned his Ph. D. in Computer Science from Georgia Institute of Technology in August 2015, under the supervision of Dr. Guy Lebanon and Prof. Hongyuan Zha. His thesis is about local approaches for collaborative filtering, with recommendation systems as the main application. He has done three internships during Ph.D, including Amazon (2014 Summer), Microsoft Research (2014 Spring), and Google (2013 Summer). Before coming to Georgia Tech, he worked in NHN corp. in Korea (2007-2010). He received his B.S degree in computer science and engineering from Seoul National University, Korea. His paper "Local Collaborative Ranking" received the best student paper award from the ACM WWW (2014) and IEEE ICDM (2016) conference. He co-organized the YouTube-8M Large-Scale Video Understanding Workshop as a program chair since 2017, and served as the publicity chair for AISTATS 2015 conference. He has served as a program committee in many conferences including NIPS, ICML, ICLR, AAAI, CVPR, I/ECCV, WSDM, and CIKM, and journals including JMLR, ACM TIST, and IEEE TKDE. More information is available in his website (http://www.joonseok.net).
Mingxuan Sun (Georgia Institute of Technology)
Seungyeon Kim (Georgia Institute of Technology)
Guy Lebanon (Amazon)
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